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Magnetic resonance imaging (MRI) of the knee, ankle, foot, and associated structures.

Model 473PH-64 Quadrature Lower Extremity Coil. Compatible with Philips Gyroscan ACS-NT 1.5T MRI System.

The provided 510(k) summary for the K99724 "Model 473PH-64 Quadrature Lower Extremity Coil" is focused on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria for algorithm performance.

Here's an analysis based on the information provided:

Acceptance Criteria and Study Details

This submission does not describe a study involving an algorithm or artificial intelligence. Instead, it details a comparison of a new medical device (an MRI coil) against an existing MRI system without that specific coil. The "acceptance criteria" are therefore framed in terms of maintaining the safety and imaging performance parameters of the predicate MRI system.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criterion for each of these parameters is implicitly "No change" or "equivalent to the predicate device performance." The reported device performance indicates that the new coil does not affect these parameters.

2. Sample Size Used for the Test Set and Data Provenance

Not applicable. This is a medical device (MRI coil) submission, not an AI/algorithm submission. There is no "test set" of patient data in the typical sense for an AI study. The evaluation is based on the technical specifications and expected performance of the MRI system with the new coil.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Not applicable. Ground truth for patient data is not established in this type of submission. The safety and performance parameters are typically assessed through engineering tests, phantom studies, and adherence to established standards for the MRI system.

4. Adjudication Method for the Test Set

Not applicable. There is no test set of patient data being adjudicated by experts.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This submission is not for an AI device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

Not applicable. This submission is for an MRI coil, not an algorithm.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

Not applicable in the context of diagnostic performance evaluation of an AI. For the device itself, the "ground truth" for its performance is its physical and operational characteristics measured against engineering specifications and the established safe operating parameters of the compatible MRI system.

8. The sample size for the training set

Not applicable. This is not an AI/algorithm submission.

9. How the ground truth for the training set was established

Not applicable. This is not an AI/algorithm submission.

Summary of the document's purpose:

This 510(k) submission demonstrates that a new MRI coil (Model 473PH-64 Quadrature Lower Extremity Coil) is substantially equivalent to an existing MRI system (Philips Gyroscan ACS-NT 1.5T MRI System) without the new coil. The core argument is that the addition of this coil does not negatively impact the established safety and imaging performance parameters of the predicate MRI system. It's a hardware submission, not a software/AI submission, hence the lack of data-driven performance metrics typically associated with AI.

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Magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) of the musculoskeletal structures, soft tissue and vascular structures of the lower extremities.

Model 473SI-42D Quadrature Lower Extremity Coil. Compatible with Siemens 1.0T Magnetom Impact MRI systems. Magnetic Resonance Imaging Coil.

The provided text is a 510(k) summary for the Model 473SI-42D Quadrature Lower Extremity Coil, a Magnetic Resonance Imaging (MRI) coil. The document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with detailed acceptance criteria and results in the way one might for a novel diagnostic algorithm.

Therefore, the information you've requested regarding acceptance criteria, study details, sample sizes, ground truth establishment, expert adjudication, and MRMC studies is not present in this type of regulatory submission for this device. This document is for a physical device (an MRI coil) whose safety and effectiveness are established by showing it performs comparably to an existing, legally marketed device and does not alter the fundamental safety parameters of the MRI system it's used with.

Here's an attempt to answer your questions based on the provided text, primarily noting what is not available due to the nature of the submission:

1. Table of Acceptance Criteria and Reported Device Performance

The device is claiming substantial equivalence to a predicate device. The "acceptance criteria" are effectively that the new device does not change the safety or imaging performance characteristics of the established MRI system when compared to the predicate device. The performance is reported as "No change" for these parameters.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the 510(k) summary. Given that this is a physical MRI coil demonstrating substantial equivalence, formal clinical "test sets" with patient data (in the sense of a diagnostic AI algorithm) are typically not required unless there are significant changes to the intended use or technology that would introduce new safety or efficacy questions. The testing would likely involve technical bench testing and phantom imaging to ensure the "no change" assertions are met.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable/not provided. The "ground truth" in this context is established by the technical specifications and performance of the Siemens 1.0T Magnetom Impact MRI system when used with the legally marketed predicate device. The goal is to show the new coil does not change these established characteristics, not to diagnose conditions from a dataset.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not applicable/not provided. Adjudication methods are typically used for clinical endpoints or diagnostic interpretations, which are not the focus of this 510(k) for an MRI coil.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic AI or imaging systems that assist human readers in making diagnoses. This submission is for an MRI coil, a hardware component.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This information is not applicable/not provided. This device is an MRI coil, a hardware component, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" in this context refers to the established safety parameters and imaging performance characteristics of the Siemens 1.0T Magnetom Impact MRI system when used with the predicate device. This is based on technical specifications, engineering measurements, and established regulatory standards for MRI equipment, rather than clinical expert consensus or pathology on patient data.

8. The sample size for the training set

This information is not applicable/not provided. This device is an MRI coil, not an AI algorithm requiring a training set.

9. How the ground truth for the training set was established

This information is not applicable/not provided. This device is an MRI coil, not an AI algorithm.

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